asciinema-analyzer

name	asciinema-analyzer
description	Semantic analysis of asciinema recordings. TRIGGERS - analyze cast, keyword extraction, find patterns in recordings.
allowed-tools	Read, Bash, Grep, Glob, AskUserQuestion

asciinema-analyzer

Semantic analysis of converted .txt recordings for Claude Code consumption. Uses tiered analysis: ripgrep (primary, 50-200ms) -> YAKE (secondary, 1-5s) -> TF-IDF (optional).

Platform: macOS, Linux (requires ripgrep, optional YAKE)

Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.

When to Use This Skill

Use this skill when:

• Searching for keywords or patterns in converted recordings
• Extracting topics or themes from session transcripts
• Finding specific commands or errors in session history
• Auto-discovering unexpected terms in recordings
• Analyzing session content for documentation or review

---

Analysis Tiers

Tier	Tool	Speed (4MB)	When to Use
1	ripgrep	50-200ms	Always start here (curated)
2	YAKE	1-5s	Auto-discover unexpected terms
3	TF-IDF	5-30s	Topic modeling (optional)

Decision: Start with Tier 1 (ripgrep + curated keywords). Only use Tier 2 (YAKE) when auto-discovery is explicitly requested.

---

Requirements

Component	Required	Installation	Notes
ripgrep	Yes	brew install ripgrep	Primary search tool
YAKE	Optional	uv run --with yake	For auto-discovery tier

---

Workflow Phases (ALL MANDATORY)

IMPORTANT: All phases are MANDATORY. Do NOT skip any phase. AskUserQuestion MUST be used at each decision point.

Phase 0: Preflight Check

Purpose: Verify input file exists and check for .txt (converted) format.

/usr/bin/env bash << 'PREFLIGHT_EOF'
INPUT_FILE="${1:-}"

if [[ -z "$INPUT_FILE" ]]; then
  echo "NO_FILE_PROVIDED"
elif [[ ! -f "$INPUT_FILE" ]]; then
  echo "FILE_NOT_FOUND: $INPUT_FILE"
elif [[ "$INPUT_FILE" == *.cast ]]; then
  echo "WRONG_FORMAT: Convert to .txt first with /asciinema-tools:convert"
elif [[ "$INPUT_FILE" == *.txt ]]; then
  SIZE=$(ls -lh "$INPUT_FILE" | awk '{print $5}')
  LINES=$(wc -l < "$INPUT_FILE" | tr -d ' ')
  echo "READY: $INPUT_FILE ($SIZE, $LINES lines)"
else
  echo "UNKNOWN_FORMAT: Expected .txt file"
fi
PREFLIGHT_EOF

If no .txt file found, suggest running /asciinema-tools:convert first.

---

Phase 1: File Selection (MANDATORY)

Purpose: Discover .txt files and let user select which to analyze.

Step 1.1: Discover .txt Files

/usr/bin/env bash << 'DISCOVER_TXT_EOF'
# Find .txt files that look like converted recordings
for file in $(fd -e txt . --max-depth 3 2>/dev/null | head -10); do
  SIZE=$(ls -lh "$file" 2>/dev/null | awk '{print $5}')
  LINES=$(wc -l < "$file" 2>/dev/null | tr -d ' ')
  BASENAME=$(basename "$file")
  echo "FILE:$file|SIZE:$SIZE|LINES:$LINES|NAME:$BASENAME"
done
DISCOVER_TXT_EOF

Step 1.2: Present File Selection (MANDATORY AskUserQuestion)

Question: "Which file would you like to analyze?"
Header: "File"
Options:
  - Label: "{filename}.txt ({size})"
    Description: "{line_count} lines"
  - Label: "{filename2}.txt ({size2})"
    Description: "{line_count2} lines"
  - Label: "Enter path"
    Description: "Provide a custom path to a .txt file"
  - Label: "Convert first"
    Description: "Run /asciinema-tools:convert before analysis"

---

Phase 2: Analysis Type (MANDATORY)

Purpose: Let user choose analysis depth.

Question: "What type of analysis do you need?"
Header: "Type"
Options:
  - Label: "Curated keywords (Recommended)"
    Description: "Fast search (50-200ms) with domain-specific keyword sets"
  - Label: "Auto-discover keywords"
    Description: "YAKE unsupervised extraction (1-5s) - finds unexpected patterns"
  - Label: "Full analysis"
    Description: "Both curated + auto-discovery for comprehensive results"
  - Label: "Density analysis"
    Description: "Find high-concentration sections (peak activity windows)"

---

Phase 3: Domain Selection (MANDATORY)

Purpose: Let user select which keyword domains to search.

Question: "Which domain keywords to search?"
Header: "Domain"
multiSelect: true
Options:
  - Label: "Trading/Quantitative"
    Description: "sharpe, sortino, calmar, backtest, drawdown, pnl, cagr, alpha, beta"
  - Label: "ML/AI"
    Description: "epoch, loss, accuracy, sota, training, model, validation, inference"
  - Label: "Development"
    Description: "iteration, refactor, fix, test, deploy, build, commit, merge"
  - Label: "Claude Code"
    Description: "Skill, TodoWrite, Read, Edit, Bash, Grep, iteration complete"

See Domain Keywords Reference for complete keyword lists.

---

Phase 4: Execute Curated Analysis

Purpose: Run Grep searches for selected domain keywords.

Step 4.1: Trading Domain

/usr/bin/env bash << 'TRADING_EOF'
INPUT_FILE="${1:?}"
echo "=== Trading/Quantitative Keywords ==="

KEYWORDS="sharpe sortino calmar backtest drawdown pnl cagr alpha beta roi volatility"
for kw in $KEYWORDS; do
  COUNT=$(rg -c -i "$kw" "$INPUT_FILE" 2>/dev/null || echo "0")
  if [[ "$COUNT" -gt 0 ]]; then
    echo "  $kw: $COUNT"
  fi
done
TRADING_EOF

Step 4.2: ML/AI Domain

/usr/bin/env bash << 'ML_EOF'
INPUT_FILE="${1:?}"
echo "=== ML/AI Keywords ==="

KEYWORDS="epoch loss accuracy sota training model validation inference tensor gradient"
for kw in $KEYWORDS; do
  COUNT=$(rg -c -i "$kw" "$INPUT_FILE" 2>/dev/null || echo "0")
  if [[ "$COUNT" -gt 0 ]]; then
    echo "  $kw: $COUNT"
  fi
done
ML_EOF

Step 4.3: Development Domain

/usr/bin/env bash << 'DEV_EOF'
INPUT_FILE="${1:?}"
echo "=== Development Keywords ==="

KEYWORDS="iteration refactor fix test deploy build commit merge debug error"
for kw in $KEYWORDS; do
  COUNT=$(rg -c -i "$kw" "$INPUT_FILE" 2>/dev/null || echo "0")
  if [[ "$COUNT" -gt 0 ]]; then
    echo "  $kw: $COUNT"
  fi
done
DEV_EOF

Step 4.4: Claude Code Domain

/usr/bin/env bash << 'CLAUDE_EOF'
INPUT_FILE="${1:?}"
echo "=== Claude Code Keywords ==="

KEYWORDS="Skill TodoWrite Read Edit Bash Grep Write"
for kw in $KEYWORDS; do
  COUNT=$(rg -c "$kw" "$INPUT_FILE" 2>/dev/null || echo "0")
  if [[ "$COUNT" -gt 0 ]]; then
    echo "  $kw: $COUNT"
  fi
done

# Special patterns
ITERATION=$(rg -c "iteration complete" "$INPUT_FILE" 2>/dev/null || echo "0")
echo "  'iteration complete': $ITERATION"
CLAUDE_EOF

---

Phase 5: YAKE Auto-Discovery (if selected)

Purpose: Run unsupervised keyword extraction.

/usr/bin/env bash << 'YAKE_EOF'
INPUT_FILE="${1:?}"
echo "=== Auto-discovered Keywords (YAKE) ==="

uv run --with yake python3 -c "
import yake

kw = yake.KeywordExtractor(
    lan='en',
    n=2,           # bi-grams
    dedupLim=0.9,  # dedup threshold
    top=20         # top keywords
)

with open('$INPUT_FILE') as f:
    text = f.read()

keywords = kw.extract_keywords(text)
for score, keyword in keywords:
    print(f'{score:.4f}  {keyword}')
"
YAKE_EOF

---

Phase 6: Density Analysis (if selected)

Purpose: Find sections with highest keyword concentration.

/usr/bin/env bash << 'DENSITY_EOF'
INPUT_FILE="${1:?}"
KEYWORD="${2:-sharpe}"
WINDOW_SIZE=100  # lines

echo "=== Density Analysis: '$KEYWORD' ==="
echo "Window size: $WINDOW_SIZE lines"
echo ""

TOTAL_LINES=$(wc -l < "$INPUT_FILE" | tr -d ' ')
TOTAL_MATCHES=$(rg -c -i "$KEYWORD" "$INPUT_FILE" 2>/dev/null || echo "0")

echo "Total matches: $TOTAL_MATCHES in $TOTAL_LINES lines"
echo "Overall density: $(echo "scale=4; $TOTAL_MATCHES / $TOTAL_LINES * 1000" | bc) per 1000 lines"
echo ""

# Find peak windows
echo "Top 5 densest windows:"
awk -v ws="$WINDOW_SIZE" -v kw="$KEYWORD" '
BEGIN { IGNORECASE=1 }
{
  lines[NR] = $0
  if (tolower($0) ~ tolower(kw)) matches[NR] = 1
}
END {
  for (start = 1; start <= NR - ws; start += ws/2) {
    count = 0
    for (i = start; i < start + ws && i <= NR; i++) {
      if (matches[i]) count++
    }
    if (count > 0) {
      printf "Lines %d-%d: %d matches (%.1f per 100)\n", start, start+ws-1, count, count*100/ws
    }
  }
}
' "$INPUT_FILE" |  -t: -k2 -rn |  -5
DENSITY_EOF

---

Phase 7: Report Format (MANDATORY)

Purpose: Let user choose output format.

Question: "How should results be presented?"
Header: "Output"
Options:
  - Label: "Summary table (Recommended)"
    Description: "Keyword counts + top 5 peak sections"
  - Label: "Detailed report"
    Description: "Full analysis with timestamps and surrounding context"
  - Label: "JSON export"
    Description: "Machine-readable output for further processing"
  - Label: "Markdown report"
    Description: "Save formatted report to file"

---

Phase 8: Follow-up Actions (MANDATORY)

Purpose: Guide user to next action.

Question: "Analysis complete. What's next?"
Header: "Next"
Options:
  - Label: "Jump to peak section"
    Description: "Read the highest-density section in the file"
  - Label: "Search for specific keyword"
    Description: "Grep for a custom term with context"
  - Label: "Cross-reference with .cast"
    Description: "Map findings back to original timestamps"
  - Label: "Done"
    Description: "Exit - no further action needed"

---

TodoWrite Task Template

1. [Preflight] Check input file exists and is .txt format
2. [Preflight] Suggest /convert if .cast file provided
3. [Discovery] Find .txt files with line counts
4. [Selection] AskUserQuestion: file to analyze
5. [Type] AskUserQuestion: analysis type (curated/auto/full/density)
6. [Domain] AskUserQuestion: keyword domains (multi-select)
7. [Curated] Run Grep searches for selected domains
8. [Auto] Run YAKE if auto-discovery selected
9. [Density] Calculate density windows if requested
10. [Format] AskUserQuestion: report format
11. [Next] AskUserQuestion: follow-up actions

---

Post-Change Checklist

After modifying this skill:

1. All bash blocks use heredoc wrapper
2. Curated keywords match references/domain-keywords.md
3. Analysis tiers match references/analysis-tiers.md
4. YAKE invocation uses uv run --with yake
5. All AskUserQuestion phases are present
6. TodoWrite template matches actual workflow

---

Reference Documentation

• Domain Keywords Reference
• Analysis Tiers Reference
• ripgrep Manual
• YAKE Documentation

---

Troubleshooting

Issue	Cause	Solution
"WRONG_FORMAT" error	.cast file provided	Run /asciinema-tools:convert first to create .txt
ripgrep not found	Not installed	brew install ripgrep
YAKE import error	Package not installed	uv run --with yake handles this automatically
No keywords found	Wrong domain selected	Try different domain or auto-discovery mode
Density analysis empty	Keyword not in file	Use curated search first to find valid keywords
File too large for YAKE	Memory constraints	Use Tier 1 (ripgrep) only for large files
Zero matches in all domains	File is binary or corrupted	Verify file is plain text with file command
fd command not found	Not installed	brew install fd or use find alternative

Post-Execution Reflection

After this skill completes, reflect before closing the task:

0. Locate yourself. — Find this SKILL.md's canonical path before editing.
1. What failed? — Fix the instruction that caused it.
2. What worked better than expected? — Promote to recommended practice.
3. What drifted? — Fix any script, reference, or dependency that no longer matches reality.
4. Log it. — Evolution-log entry with trigger, fix, and evidence.

Do NOT defer. The next invocation inherits whatever you leave behind.